User input within the network training loop
This category of work injects the user input as the additional input for training the network. Through this way, no online fine-tuning is needed. These algorithms incorporate the user input either by using a parallel network or concatenating the image with the user input [1]}, [2]}. One drawback of this kind of methods is that the model needs to be recalculated once the user input changes, thus it is not practical for adaptation especially for long videos.
